Testing in Production

Detailed explanation

Testing in Production (TiP) is a software testing approach where testing activities are performed in a live, production environment. Unlike traditional testing phases that occur before deployment, TiP involves exposing the application to real users and real-world conditions. This strategy is particularly valuable for identifying issues that are difficult or impossible to replicate in controlled testing environments, such as performance bottlenecks under heavy load, unexpected user behavior, or integration problems with external systems.

TiP is not a replacement for pre-production testing, but rather a complementary approach that enhances the overall testing strategy. It's crucial to have robust pre-production testing in place, including unit tests, integration tests, and system tests, before considering TiP. The goal of TiP is to catch the "unknown unknowns" – the issues that slip through the cracks of traditional testing.

Why Test in Production?

Several factors drive the adoption of TiP:

• Real-world conditions: Production environments are inherently complex and dynamic. They involve real users, diverse network conditions, and integrations with various external systems. These factors can be difficult to simulate accurately in pre-production environments.
• High fidelity data: Production data is often more realistic and diverse than test data. This can expose issues related to data quality, data volume, or data integrity.
• Continuous Delivery: In modern software development practices like Continuous Delivery, features are deployed frequently. TiP allows for faster feedback loops and quicker identification of issues in a live environment.
• Unforeseen User Behavior: Users interact with applications in ways that developers and testers may not anticipate. TiP can reveal unexpected usage patterns and edge cases.

Common Techniques for Testing in Production

Several techniques can be employed for TiP, each with its own advantages and disadvantages:

• Canary Releases: A canary release involves deploying a new version of the application to a small subset of users. This allows you to monitor the performance and stability of the new version before rolling it out to the entire user base. If issues are detected, the canary release can be quickly rolled back.

  # Example: Canary deployment using Kubernetes
  apiVersion: apps/v1
  kind: Deployment
  metadata:
    name: my-app-stable
  spec:
    replicas: 10
    selector:
      matchLabels:
        app: my-app
    template:
      metadata:
        labels:
          app: my-app
      spec:
        containers:
        - name: my-app
          image: my-app:v1.0
  
  ---
  apiVersion: apps/v1
  kind: Deployment
  metadata:
    name: my-app-canary
  spec:
    replicas: 2 # Small subset of users
    selector:
      matchLabels:
        app: my-app
    template:
      metadata:
        labels:
          app: my-app
      spec:
        containers:
        - name: my-app
          image: my-app:v1.1 # New version

• Feature Flags: Feature flags (also known as feature toggles) allow you to enable or disable features in production without deploying new code. This is useful for testing new features with a small group of users or for quickly disabling a feature if it causes problems.

  # Example: Feature flag using a simple dictionary
  feature_flags = {
      "new_checkout_flow": True,
      "discount_engine": False
  }
   
  def process_order(user):
      if feature_flags["new_checkout_flow"]:
          checkout_result = new_checkout(user)
      else:
          checkout_result = old_checkout(user)
   
      if feature_flags["discount_engine"]:
          apply_discount(checkout_result)
   
      return checkout_result

• A/B Testing: A/B testing involves presenting different versions of a feature or application to different groups of users and measuring their response. This can be used to optimize user experience, improve conversion rates, or test the effectiveness of new features.

• Shadow Traffic: Shadow traffic involves sending a copy of production traffic to a test environment. This allows you to test new features or infrastructure changes without impacting real users. The results of the shadow traffic are analyzed to identify potential issues.

• Synthetic Monitoring: Synthetic monitoring involves creating automated tests that simulate user behavior in production. These tests can be used to monitor the availability, performance, and functionality of the application.

Best Practices for Testing in Production

• Minimize Impact: TiP should be performed in a way that minimizes the impact on real users. This can be achieved by using techniques like canary releases, feature flags, and shadow traffic.
• Monitor Closely: It's crucial to monitor the application closely during TiP to identify any issues that may arise. This includes monitoring performance metrics, error logs, and user feedback.
• Automate Rollbacks: Have automated mechanisms in place to quickly rollback changes if issues are detected.
• Data Privacy and Security: Ensure that TiP activities comply with data privacy and security regulations. Avoid using sensitive data in testing, or anonymize it before use.
• Communicate Clearly: Communicate with users about TiP activities, especially if they may be affected. Be transparent about the purpose of the testing and the potential impact.
• Implement Circuit Breakers: Use circuit breakers to prevent cascading failures. If a service or component fails, the circuit breaker will prevent further requests from being sent to it, protecting the rest of the system.
• Use Observability Tools: Employ robust observability tools for monitoring, logging, tracing, and alerting. Tools like Prometheus, Grafana, Jaeger, and ELK stack are essential for understanding the behavior of the application in production.

Common Tools

• LaunchDarkly: A feature flag management platform.
• Split.io: Another feature flag and experimentation platform.
• Optimizely: A platform for A/B testing and personalization.
• New Relic, Datadog, Dynatrace: Application Performance Monitoring (APM) tools.
• Prometheus and Grafana: Open-source monitoring and alerting tools.
• Jaeger and Zipkin: Distributed tracing tools.
• Kubernetes: Container orchestration platform for canary deployments.

TiP is a powerful technique for improving the quality and reliability of software. By testing in a live environment, you can uncover issues that may not be found in pre-production environments and ensure that your application meets the needs of your users. However, it's important to approach TiP with caution and to follow best practices to minimize the impact on real users.

Further reading

• Testing in Production, the safe way
• Feature Toggles (aka Feature Flags)
• Canary Release
• Testing in Production: Risks and Mitigation Strategies